Causes of Death and Conditional Survival of Renal Cell Carcinoma

Establishment and validation of a novel disulfidptosis-related immune checkpoint gene signature in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal tumors and is associated with a unfavorable prognosis. Disulfidptosis is a recently identified form of cell death mediated by disulfide bonds. Numerous studies have highlighted the significance of immune checkpoint genes (ICGs) in ccRCC. Nevertheless, the involvement of disulfidptosis-related immune checkpoint genes (DRICGs) in ccRCC remains poorly understood.

METHODS

The mRNA expression profiles and clinicopathological data of ccRCC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. The associations between disulfidptosis-related genes (DRGs) and immune checkpoint genes (ICGs) were assessed to identify DRICGs. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were conducted to construct a risk signature.

RESULTS

A total of 39 differentially expressed immune-related candidate genes were identified. A prognostic signature was constructed utilizing nine DRICGs (CD276, CD80, CD86, HLA-E, LAG3, PDCD1LG2, PVR, TIGIT, and TNFRSF4) and validated using GEO data. The risk model functioned as an independent prognostic indicator for ccRCC, while the associated nomogram provided a reliable scoring system for ccRCC. Gene set enrichment analysis indicated enrichment of phospholipase D, antigen processing and presentation, and ascorbate and aldarate metabolism-related signaling pathways in the high-risk group. Furthermore, the DRICGs exhibited correlations with the infiltration of various immune cells. It is noteworthy that patients with ccRCC categorized into distinct risk groups based on this model displayed varying sensitivities to potential therapeutic agents.

CONCLUSIONS

The novel DRICG-based risk signature is a reliable indicator for the prognosis of ccRCC patients. Moreover, it also aids in drug selection and correlates with the tumour immune microenvironment in ccRCC.

Factors influencing the diagnostic and prognostic values of circulating tumor cells in breast cancer: a meta-analysis of 8,935 patients

Background

Circulating tumor cells (CTCs) could serve as a predictive biomarker in breast cancer (BC). Due to its high heterogeneity, the diagnostic and prognostic values of CTC are challenging.

Methods

We searched published studies from the databases of PubMed, Cochrane Library, Embase, and MEDLINE. The detection capability and hazard ratios (HRs) of CTCs were extracted as the clinical diagnosis and prognosis evaluation. Subgroup analyses were divided according to the detection methods, continents, treatment periods, therapeutic plans, and cancer stages.

Results

In this study, 35 publications had been retrieved with 8,935 patients enrolled. The diagnostic efficacy of CTC detection has 74% sensitivity and 98% specificity. The positive CTC detection (CTC+ ) would predict worse OS and PFS/DFS in both mid-therapy and post-therapy (HROS, 3.09; 95% CI, 2.17–4.39; HRPFS/DFS, 2.06; 95% CI, 1.72–2.47). Moreover, CTC+ indicated poor survival irrespective of the treatment phases and sampling times (HROS, 2.43; 95% CI, 1.85–3.19; HRPFS/DFS, 1.82; 95% CI, 1.66–1.99). The CTC+ was associated with poor survival regardless of the continents of patients (HROS = 2.43; 95% CI, 1.85–3.19).

Conclusion

Our study suggested that CTC+ was associated with a worse OS and PFS/DFS in the Asian population. The detection method, the threshold level of CTC+ , therapeutic approaches, and sampling times would not affect its diagnostic and prognostic values.

Racial disparities in conditional survival of patients with bladder cancer: a population-based study

BACKGROUND

Traditional estimates can only provide static predictions of cancer outcomes and cannot assess the evolving effect of race on patient survival. This study aims to reveal the dynamic survival of patients with bladder cancer and to explore the evolving effect of race on patient prognosis.

METHODS

Using data from the Surveillance, Epidemiology, and End Results (SEER) registry, 99,590 white, 6,036 African American, and 4,685 Asian/Pacific Islander (API) patients with bladder cancer were identified. Conditional cancer-specific survival (CSS) rates, which could reflect the dynamic survival prediction of cancer patients, represented the primary outcomes, and were estimated by the Kaplan-Meier algorithm. The evolving effect of race on patient survival was evaluated by multivariable Cox regression in combination with conditional survival (CS) estimates.

RESULTS

The 5-year CSS for African American patients who had survived 1, 2, 3, 4, or 5 years after definitive therapy improved from the baseline calculation by + 5.8 (84.4%), + 9.5 (87.4%), + 12.8 (90.0%), + 14.4 (91.3%), and + 14.7% (91.5%), respectively. The increasing trend also held for overall white and API patients, and for all patient subsets when CS was calculated according to different levels of sex, age, and disease stage. African Americans, despite having the worst survival at baseline, could have CSS comparable to their white and API counterparts after 4 years of survivorship. In addition, the risk of death for African Americans tended to decrease with increasing survival, and the risk was no longer significantly different from that of whites after 4 years of survival.

CONCLUSIONS

While having the worst initial predicted outcomes, African Americans may eventually achieve comparable survival to white and API patients given several years of survivorship. As patient survival increases, African American race may lose its role as an indicator of poorer prognosis.

The Impact of COVID-19 on New Kidney Cancer Diagnosis: Stage and Treatment in Northern Italy

This study aims to evaluate the impact of COVID-19 on new renal carcinoma (RC) diagnoses using data from the Reggio Emilia Cancer Registry in 2018-2020. A total of 293 RCs were registered, with roughly 100 cases yearly. The distribution by age shows a significant decrease in the 30-59 age group (33.7% in 2018, 24.8% in 2019, and 19.8% in 2020). The incidence of Stage I was 59.4%, 46.5%, and 58.2% in 2018, 2019, and 2020, respectively, whereas the Stage II rate had values of 6.9%, 7.9%, and 2.2% in the years 2018, 2019, and 2020, respectively. Slight non-significant variations were observed in Stages III and IV. Surgery was performed in 83.2% of cases in 2018, 78.2% in 2019, and 82.4% in 2020; the surgery distribution by stage showed no significant differences. Chemotherapy showed an increase in 2020, which was statistically significant only for Stage IV. The gender incidence trends over the last 25 years showed an increase in the male sex in the first period; then, a decline was documented, likely due to a decrease in cigarette consumption. In females, the trend was constant. The RC mortality trend significantly dropped in both genders over the entire study period.

Cost-effectiveness analysis: percutaneous microwave ablation vs robotic-assisted partial nephrectomy for small renal masses

PURPOSE

The majority of newly diagnosed renal tumors are masses < 4 cm in size with treatment options, including active surveillance, partial nephrectomy, and ablative therapies. The cost-effectiveness literature on the management of small renal masses (SRMs) does not account for recent advances in technology and improvements in technical expertise. We aim to perform a cost-effectiveness analysis for percutaneous microwave ablation (MWA) and robotic-assisted partial nephrectomy (RA-PN) for the treatment of SRMs.

METHODS

We created a decision analytic Markov model depicting management of the SRM incorporating costs, health utilities, and probabilities of complications and recurrence as model inputs using TreeAge. A willingness to pay (WTP) threshold of $100,000 and a lifetime horizon were used. Probabilistic and one-way sensitivity analyses were performed.

RESULTS

Percutaneous MWA was the preferred treatment modality. MWA dominated RA-PN, meaning it resulted in more quality-adjusted life years (QALYs) at a lower cost. Cost-effectiveness analysis revealed a negative Incremental Cost-Effectiveness Ratio (ICER), indicating dominance of MWA. The model revealed MWA had a mean cost of $8,507 and 12.51 QALYs. RA-PN had a mean cost of $21,521 and 12.43 QALYs. Relative preference of MWA was robust to sensitivity analysis of all other variables. Patient starting age and cost of RA-PN had the most dramatic impact on ICER.

CONCLUSION

MWA is more cost-effective for the treatment of SRM when compared with RA-PN and accounting for complication and recurrence risk.

Cause of death during upper tract urothelial carcinoma survivorship: A contemporary, population-based analysis

Background

Very few studies have been published on the causes of death of upper tract urothelial carcinoma (UTUC). We sought to explore the mortality patterns of contemporary UTUC survivors.

Methods

We performed a retrospective cohort study involving patients with upper urinary tract carcinoma from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database (2000 and 2015). We used standardized mortality ratios (SMRs) to compare death rates among patients with UTUC in the general population and excess absolute risks (EARs) to quantify the disease-specific death burden.

Results

A total of 10,179 patients with UTUC, including 7,133 who died, were included in our study. In total, 302 (17.17%) patients with the localized disease died of UTUC; however, patients who died from other causes were 4.8 times more likely to die from UTUC (n = 1,457 [82.83%]). Cardiovascular disease was the most common non-cancer cause of death (n = 393 [22.34% of all deaths]); SMR, 1.22; 95% confidence intervals [CI], 1.1–1.35; EAR, 35.96). A total of 4,046 (69.99%) patients with regional stage died within their follow-up, 1,413 (34.92%) of whom died from UTUC and 1,082 (26.74%) of whom died from non-cancer causes. UTUC was the main cause of death (SMR, 242.48; 95% CI, 230–255.47; EAR, 542.47), followed by non-tumor causes (SMR, 1.18; 95% CI, 1.11–1.25; EAR, 63.74). Most patients (94.94%) with distant stage died within 3 years of initial diagnosis. Although UTUC was the leading cause of death (n = 721 [54.29%]), these patients also had a higher risk of death from non-cancer than the general population (SMR, 2.08; 95% CI, 1.67–2.56; EAR, 288.26).

Conclusions

Non-UTUC deaths accounted for 82.48% of UTUC survivors among those with localized disease. Patients with regional/distant stages were most likely to die of UTUC; however, there is an increased risk of dying from non-cancer causes that cannot be ignored. These data provide the latest and most comprehensive assessment of the causes of death in patients with UTUC.

Development and validation of a novel immunotype for prediction of overall survival in patients with clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a highly immunogenic tumor. The purpose of the present study was to establish a novel immunotype for different immune infiltration and overall survival (OS) of patients with ccRCC.

Methods

Based on the Cancer Genome Atlas Project (TCGA) database (discovery set), a novel immunotype was established using ssGSEA methods. The databases of Fudan University Shanghai Cancer Center (FUSCC) and Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine (XHH) served as an external validation set. GSEA was carried out to identify the immunotype associated signal transduction pathways.

Results

A total of 652 ccRCC patients were included in our study. We constructed a novel immunotype of ccRCC to classify patients into three groups: high-immunity, moderate-immunity, and low-immunity. The high-immunity and moderate-immunity groups had higher ImmuneScores, ESTIMATEScores, StromalScores, and lower tumor purity than that of the low-immunity group in both sets. Additionally, the patients from the high-immunity and moderate-immunity groups had longer survival than patients from low-immunity group in both discovery set and validation set (HR = 2.54, 95% CI: 1.56–4.13, p < 0.01; HR = 2.75, 95% CI: 1.24–6.11, p = 0.01).

Conclusion

In summary, we defined a novel immunotype of ccRCC. The immune types could be used as a clinical predictive tool to identify ccRCC patients with different survival. In addition, the immune-related biological signaling pathway also brought new insights on the mechanism of ccRCC.

Trends in Incidence and Mortality of Kidney Cancer in a Northern Italian Province: An Update to 2020

The aim of this study was to examine the incidence and mortality trends for tumors and cardiovascular disease (CVD) in a province of northern Italy. The study included kidney cancers recorded in the period 1996−2020, divided by sex, age, year of incidence and years from diagnosis. The standardized incidence rate was calculated using the European population, and the Annual Percent Change (APC) was reported. In total, 2331 patients with kidney cancers were identified, mainly males (1504 cases) aged 60−79 years (1240 cases). There were 1257 deaths; there were no differences according sex but there were differences according to age (12.1% among younger adults and 80.4% among 80+). The incidence rate increased in males between 1996 and 2011 (APC = 2.3), while the mortality rate decreased in both males (APC = −3.3%) and females (APC = −4.5%). Comparing the same periods, kidney cancer-specific mortality decreased from 81.8% to 43.7%, while in the same period there was an increasing trend for CVD mortality. Moreover, the risk of CVD mortality increased as we moved away from the diagnosis (from 6.2% to 27.5%, p < 0.01). The same trend was observed for other causes of death (from 12.6% to 32.1%, p < 0.01). Thus, a multidisciplinary approach seems necessary during the follow-up and treatments of patients with kidney cancer.

Cause of Death During Renal Cell Carcinoma Survivorship: A Contemporary, Population-Based Analysis

Background

As the survival rates of patients with renal cell carcinoma (RCC) continue to increase, noncancer causes of death cannot be ignored. The cause-specific mortality in patients with RCC is not well understood.

Objective

Our study aimed to explore the mortality patterns of contemporary RCC survivors.

Methods

We performed a retrospective cohort study involving patients with RCC from the Surveillance, Epidemiology, and End Results (SEER) database. We used standardized mortality ratios (SMRs) to compare the death rates in patients with RCC with those in the general population.

Results

A total of 106,118 patients with RCC, including 39,630 who died (27%), were included in our study. Overall, compared with the general US population, noncancer SMRs were increased 1.25-fold (95% confidence intervals [CI], 1.22 to 1.27; observed, 11,235), 1.19-fold (95% CI, 1.14 to 1.24; observed, 2,014), and 2.24-fold (95% CI, 2.11 to 2.38; observed, 1,110) for stage I/II, III, and IV RCC, respectively. The proportion of noncancer causes of death increased with the extension of survival time. A total of 4,273 men with stage I/II disease (23.13%) died of RCC; however, patients who died from other causes were 3.2 times more likely to die from RCC (n = 14,203 [76.87%]). Heart disease was the most common noncancer cause of death (n = 3,718 [20.12%]; SMR, 1.23; 95% CI, 1.19–1.27). In patients with stage III disease, 3,912 (25.98%) died from RCC, and 2,014 (13.37%) died from noncancer causes. Most patients (94.99%) with stage IV RCC died within 5 years of initial diagnosis. Although RCC was the leading cause of death (n = 12,310 [84.65%]), patients with stage IV RCC also had a higher risk of noncancer death than the general population (2.24; 95% CI, 2.11–2.38).

Conclusions

Non-RCC death causes account for more than 3/4 of RCC survivors among patients with stage I/II disease. Patients with stage IV are most likely to die of RCC; however, there is an increased risk of dying from septicemia, and suicide cannot be ignored. These data provide the latest and most comprehensive assessment of the causes of death in patients with RCC.

Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models

As the world moves into the exciting age of Healthcare 4.0, it is essential that patients and clinicians have confidence and reassurance that the real-time clinical decision support systems being used throughout their care guarantee robustness and optimal quality of care. However, current systems involving autonomic behaviour and those with no prior clinical feedback, have generally to date had little focus on demonstrating robustness in the use of data and final output, thus generating a lack of confidence. This paper wishes to address this challenge by introducing a new process mining approach based on a statistically robust methodology that relies on the utilisation of conditional survival models for the purpose of evaluating the performance of Healthcare 4.0 systems and the quality of the care provided. Its effectiveness is demonstrated by analysing the performance of a clinical decision support system operating in an intensive care setting with the goal to monitor ventilated patients in real-time and to notify clinicians if the patient is predicted at risk of receiving injurious mechanical ventilation. Additionally, we will also demonstrate how the same metrics can be used for evaluating the patient quality of care. The proposed methodology can be used to analyse the performance of any Healthcare 4.0 system and the quality of care provided to the patient.

Does histologic subtype impact overall survival in observed T1a kidney cancers compared with competing risks? Implications for biopsy as a risk stratification tool

OBJECTIVES

We sought to assess if adding a biopsy proven histologic subtype to a model that predicts overall survival that includes variables representing competing risks in observed, biopsy proven, T1a renal cell carcinomas, enhances the model's performance.

METHODS

The National Cancer Database was assessed (years 2004-2015) for patients with observed T1a renal cell carcinoma who had undergone renal mass biopsy. Kaplan-Meier curves were utilized to estimate overall survival stratified by histologic subtype. We utilized C-index from a Cox proportional hazards model to evaluate the impact of adding histologic subtypes to a model to predict overall survival for each stage.

RESULTS

Of 132 958 T1a renal masses identified, 1614 had biopsy proven histology and were managed non-operatively. Of those, 61% were clear cell, 33% papillary, and 6% chromophobe. Adjusted Kaplan-Meier curves demonstrated a difference in overall survival between histologic subtypes (P = 0.010) with greater median overall survival for patients with chromophobe (85.1 months, hazard rate 0.45, P = 0.005) compared to clear cell (64.8 months, reference group). Adding histology to a model with competing risks alone did not substantially improve model performance (C-index 0.65 vs 0.64 respectively).

CONCLUSIONS

Incorporation of histologic subtype into a risk stratification model to determine prognostic overall survival did not improve modeling of overall survival compared with variables representing competing risks in patients with T1a renal cell carcinoma managed with observation. These results suggest that performing renal mass biopsy in order to obtain tumor histology may have limited utility. Future studies should further investigate the overall utility of renal mass biopsy for observed T1a kidney cancers.

Differential VHL mutation patterns in bilateral clear cell RCC distinguishes between independent primary tumors and contralateral metastatic disease

OBJECTIVE

To evaluate whether bilateral, multifocal clear cell renal cell carcinoma (ccRCC) patients can be differentiated by VHL mutation analysis into cases that represent either multiple independently arising primary tumors, or a single primary tumor which has spread ipsilaterally as well as to the contralateral kidney. The nature of kidney cancer multifocality outside of known hereditary syndromes is as yet poorly understood.

MATERIALS AND METHODS

DNA from multiple tumors per patient were evaluated for somatic VHL gene mutation and hypermethylation. A subset of tumors with shared VHL mutations were analyzed with targeted, next-generation sequencing assays.

RESULTS

This cohort contained 5 patients with multiple tumors that demonstrated a shared somatic VHL mutation consistent with metastatic spread including to the contralateral kidney. In several cases this was substantiated by additional shared somatic mutations in ccRCC-associated genes. In contrast, the remaining 14 patients with multiple tumors demonstrated unique, unshared VHL alterations in every analyzed tumor, consistent with independently arising kidney tumors. None of these latter patients showed any evidence of local spread or distant metastasis.

CONCLUSION

The spectrum of VHL alterations within evaluated bilateral, multifocal ccRCC tumors from a single patient can distinguish between multiple independent tumor growth and metastasis. This can be performed using currently available clinical genetic tests and will improve the accuracy of patient diagnosis and prognosis, as well as informing appropriate management.

Conditional cancer-specific survival after radical hepatectomy in patients with nonmetastatic intrahepatic cholangiocarcinoma

OBJECTIVES

To examine conditional survival (CS) in patients with non-metastatic intrahepatic cholangiocarcinoma (nmICC) after surgical treatment according to pT and pN stages.

METHODS

A total of 608 patients were included. Conditional three-year cancer-specific mortality estimates were obtained according to pT and pN stages. Multivariable Cox regression analysis was applied to predict factors affecting cancer-specific mortality (CSM).

RESULTS

According to substages based on pT and pN status, 109 patients (17.9%) with pT1aN0, 96 (15.8%) with pT1bN0, 205 (33.7%) with pT2N0, 82(13.5%) with pT3-5N0, and 116 (19.1%) with pTanyN1 were identified. Conditional CSM-free estimates increased from 75% to 87%, 66% to 70%, 53% to 86%, and 36 to 54% after three years of event-free follow-up in pT1b, pT2, pT3-4N0, and pTanyN1 patients, respectively, whereas it decreased from 87% to 79% in pT1aN0 patients. Based on multivariable analysis, patients with pT2N0 (hazard ratio [HR] 2.0 p < .01), pT3-4N0 (HR 2,7 p < .01), and pTanyN1-3 (HR 4.8 p < .01) had higher CSM than patients with pT1aN0 at baseline.

CONCLUSIONS

CS varied across stage categories in nmICC patients after surgery, and it was important in individualized clinical counseling and decision-making for nmICC.

A Simple-To-Use Nomogram for Predicting Early Death in Metastatic Renal Cell Carcinoma: A Population-Based Study

Background

Metastatic renal cell carcinoma (mRCC) is usually considered to have a poor prognosis, which has a high risk of early death (≤3 months). Our aim was to developed a predictive nomogram for early death of mRCC.

Methods

The SEER database was accessed to obtain the related information of 6,005 mRCC patients between 2010 and 2015. They were randomly divided into primary cohort and validation cohort in radio of 7:3. The optimal cut-off point regarding age at diagnosis and tumor size were identified by the X-tile analysis. Univariate and multivariate logistic regression models were applied to determine significant independent risk factors contributed to early death. A practical nomogram was constructed and then verified by using calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA).

Results

There were 6,005 patients with mRCC included in the predictive model, where 1,816 patients went through early death (death within ≤3 months of diagnosis), and among them 1,687 patients died of mRCC. Based on 11 significant risk factors, including age, grade, N-stage, histologic type, metastatic sites (bone, lung, liver and brain) and treatments (surgery, radiation, and chemotherapy), a practical nomogram was developed. The model's excellent effectiveness, discrimination and clinical practicality were proved by the AUC value, calibration plots and DCA, respectively.

Conclusions

The nomogram may play a major part in distinguishing the early death of mRCC patients, which can assist clinicians in individualized medicine.

Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer

About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite different, even in patients with kidney cancer at the same clinical stage. Therefore, there is an urgent need to screen for kidney cancer prognosis biomarkers. Differentially expressed genes (DEGs) were identified using kidney cancer RNA sequencing data from the Gene Expression Omnibus (GEO) database. Biomarkers were screened using random forest (RF) and support vector machine (SVM) models, and a multigene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. Univariate and multivariate Cox regression analyses were performed to explore the relationships between clinical features and prognosis. Finally, the reliability and clinical applicability of the model were validated, and relationships with biological pathways were identified. Western blots were also performed to evaluate gene expression. A total of 50 DEGs were obtained by intersecting the RF and SVM models. A seven-gene signature (RNASET2, EZH2, FXYD5, KIF18A, NAT8, CDCA7, and WNT7B) was constructed by LASSO regression. Univariate and multivariate Cox regression analyses showed that the seven-gene signature was an independent prognostic factor for kidney cancer. Finally, a predictive nomogram was established in The Cancer Genome Atlas (TCGA) cohort and validated internally. In tumor tissue, RNASET2 and FXYD5 were highly expressed and NAT8 was lowly expressed at the protein and transcription levels. This model could complement the clinicopathological characteristics of kidney cancer and promote the personalized management of patients with kidney cancer.

Overall Survival of Biopsy-confirmed T1B and T2A Kidney Cancers Managed With Observation: Prognostic Value of Tumor Histology

INTRODUCTION

The natural history of T1b (4-7 cm) or T2a (> 7-10 cm) kidney cancers managed with observation is not well-understood. The aim of our study was to determine if the addition of histologic subtype to a predictive model of overall survival (OS) that includes covariates for competing risks in observed, biopsy-proven, T1b and T2a renal cell carcinomas (RCCs) improves the model's performance.

MATERIALS AND METHODS

We queried the National Cancer Database for patients with biopsy-proven stage T1b or T2a RCC and managed nonoperatively between 2004 and 2015. OS was estimated by Kaplan-Meier curves based on histologic subtype. The concordance index (c-index) from a Cox proportional hazards model was used to estimate the extent to which histologic subtypes predict survival for each stage when included in a model along with competing risks of age, gender, race/ethnicity, insurance status, area-level socioeconomic indicators, Charlson-Deyo index, and tumor grade.

RESULTS

A total of 937 patients (754 with T1b and 185 with T2a) with biopsy-proven RCC were identified. Kaplan-Meier analysis suggested differences in OS by histologic subtype where sarcomatoid, followed by clear cell, papillary, and chromophobe, had the highest mortality risk at 1, 3, and 5 years. However, there was marginal improvement in the multivariable model of OS using competing risks and histology (c-index, 0.64 and 0.697) compared with competing risks alone (c-index, 0.631 and 0.671) for T1b and T2a RCCs, respectively.

CONCLUSIONS

In patients with T1b or T2a RCC managed with observation, incorporation of histologic subtype into a risk-stratification model to determine prognostic OS did not improve modeling of OS compared with variables representing competing risks. Histologic subtype of observed T1b and T2a RCC appears to have prognostic OS value when not considering competing risks. These findings may impact the usefulness of renal biopsy to inform decision-making when managing patients with T1b and T2a renal tumors with observation.

Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients

PURPOSE

The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients.

METHODS

According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models.

RESULTS

The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively).

CONCLUSIONS

We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens.

Conditional Survival of female patients with operable invasive Breast Cancer in US: A population-based study

Background: Conditional survival (CS) is used to describe the dynamic possibility of survival, considering the changes of death risk with time lapsing. This study aimed to estimate the conditional disease-specific survival (CDS) for the female with operable invasive breast cancer.

Methods: The data was obtained from Surveillance, Epidemiology, and End Result Program of the National Cancer Institute. The hazard rate was calculated using kernel density smoothing method. The disease-specific survival (DSS) rates were estimated and compared using Kaplan-Meier method and log-rank test. The Cox regression model was used to adjust confounding factors. The CDS was calculated by CDS(y|x)=DSS(x + y)/DSS(x), where DSS(x) representatives the DSS at x year.

Results: The 5-year, 10-year, and 15-year DSS was 88.7%, 82.0%, and 78.3%, respectively. The hazard rate after surgery increased initially and peaked at about 1.5 years, then decreased gradually. Meanwhile, the CDS decreased just after surgery then increased continuously, which showed a contrary trend with hazard rate. Patients with high risk factors had greater survival gap between cumulative DSS and CDS. The changing trend of CDS in patients with high risk factors was more significant, and the CDS gap between low-risk patients and high-risk patients gradually decreased over time.

Conclusion: CS could provide a more precise long-term prognostic evaluation compared to traditional cumulative survival, especially for long-time survivors with high risk.

Conditional survival after surgery for patients with penile cancer

BACKGROUND

Penile cancer represents a rare pathology whose natural history of treatment is poorly understood.

OBJECTIVE

To illustrate the dynamic survival profiles in surgically treated patients with squamous cell carcinoma of the penis (SCCP) using the conditional survival (CS) estimates.

MATERIALS AND METHODS

Patients with non-metastatic SCCP were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Conditional 3-yr overall survival (OS) rate and 3-yr cancer-specific survival (CSS) rate represented the primary outcomes of interest and were calculated using the Kaplan-Meier method. The multivariable Cox regression model was employed to calculate proportional hazard ratios for the prediction of mortality.

RESULTS

A total of 1887 SCCP patients who had undergone surgeries were identified. Given a 1-, 2-, 3-, 4-, and 5-yr survivorship, the 3-yr OS rates were, respectively, improved by + 9.8 (72.6%), +18.2 (78.1%), +23.4 (81.6%), +27.8 (84.5%), and + 26.6% (83.7%) from those calculated at baseline (time zero). As compared with the baseline calculations, patients who had survived 1, 2, 3, 4, or 5 yr after surgery could, respectively, harvest a + 7.8 (84.7%), +14.8 (90.2%), +19.5 (93.9%), +22.1 (96.0%), and + 22.4% (96.2%) improvement in 3-yr CSS. Patients with the most aggressive disease at baseline ultimately benefited the most from event-free survivorship. Multivariable Cox regression analyses showed that the impact of adverse pathological parameters (G2-3, ≥ pT2, pN+) on OS and CSS mostly showed a decreasing trend over time and some could disappear after a minimum of 1-yr survivorship.

DISCUSSION AND CONCLUSION

The survival probability of SCCP patients increases with post-operative survival. Patients with aggressive disease at baseline ultimately benefit the most from event-free survivorship and may expect a better prognosis once they survive the critical few years after surgery. The recorded observations have crucial implications regarding patient counseling and follow-up.

Aberrant Expression of miR-592 Is Associated with Prognosis and Progression of Renal Cell Carcinoma

Purpose

MicroRNAs have recently reported playing a vital role in the development of cancers. However, the role of miR-592 in renal cell carcinoma (RCC) has not been explored. In this study, the potential role of miR-592 was investigated in RCC.

Patients and methods

The expression of miR-592 was evaluated in RCC tissues and cell lines using qRT-PCR assays. The Kaplan-Meier analysis and Cox proportional hazards model analysis was used to analyze the prognostic value of miR-592 in RCC. The effects of miR-592 on cell proliferation, migration, and invasion were determined by cell counting kit-8 (CCK-8) and Transwell assays in vitro.

Results

The results showed that miR-592 was significantly increased both in RCC tissues and cell lines. Overexpression of miR-592 was significantly associated with lymph node metastasis, TNM stage, and poor overall survival. And functional studies in two RCC cell lines (786-O and Caki-1) have shown that overexpression of miR-592 promoted cell proliferation, migration, and invasion, while silence of miR-592 inhibited cell proliferation, migration, and invasion. SPRY2 was a direct target of miR-592.

Conclusion

Overall, overexpression of miR-592 may be a prognostic biomarker and therapeutic strategy for patients with RCC, which is correlated with the progression of RCC.